Nemesure B B, Greenberg D A, Mendell N R
Department of Preventive Medicine, State University of New York at Stony Brook 11794-8275, USA.
Genet Epidemiol. 1995;12(4):351-9. doi: 10.1002/gepi.1370120403.
Three interval estimation procedures were evaluated to determine the method which provides the most accurate estimates for the recombination fraction, 0. The lod-0.83 support interval, the jackknife confidence interval, and the confidence interval based on estimated asymptotic standard error were compared by calculating the coverage probabilities of each. Family data that were simulated under the model of a single fully penetrant, dominant disease locus at some distance, 0, from fully informative matings were used. Comparisons were based on 1,000 random samples of size 20,60, and 100 families. In addition, a methodology for obtaining prediction intervals for 0 was developed. This procedure is of practical use and does not require asymptotic assumptions based on large sample theory. The results provide an a priori idea about precision of the estimates, as well as empirical interval estimates of 0. Graphs of the authors' Monte Carlo intervals are presented for these simulations. Investigators studying different traits, however, could condition specifically on the family structure and distribution of the disease they are investigating and obtain similar graphs.
评估了三种区间估计程序，以确定能为重组率θ提供最准确估计值的方法。通过计算每种方法的覆盖概率，比较了lod-0.83支持区间、刀切法置信区间和基于估计渐近标准误差的置信区间。使用了在单个完全显性疾病位点模型下模拟的家系数据，该位点距完全信息交配位点有一定距离θ。比较基于大小为20、60和100个家系的1000个随机样本。此外，还开发了一种获取θ预测区间的方法。该程序具有实际用途，且不需要基于大样本理论的渐近假设。结果提供了关于估计精度的先验概念，以及θ的经验区间估计。针对这些模拟给出了作者蒙特卡罗区间的图形。然而，研究不同性状的研究者可以根据他们所研究疾病的家系结构和分布进行具体条件设定，并获得类似的图形。
